KSLCore/ksl.utilities.distributions.fitting.estimators/WeibullMLEParameterEstimator

WeibullMLEParameterEstimator

class WeibullMLEParameterEstimator(name: String? = "WeibullMLEParameterEstimator") : ParameterEstimatorIfc, MVBSEstimatorIfc, IdentityIfc

Estimates the parameters of the Weibull distribution based on a MLE algorithm. See page 287-288 of Law (2007) Simulation Modeling and Analysis. Uses Newton steps followed by bi-section search (if needed). Convergence is not guaranteed and will be indicated in the EstimatedParameters success property and the message. Requires that the data be strictly positive and that there are at least two observations. Also, requires that all the supplied data are not equal. The user may vary some of the search control parameters to assist with convergence.

Constructors

constructor(name: String? = "WeibullMLEParameterEstimator")

Properties

checkRange

open override val checkRange: Boolean = true

Indicates if the estimator requires that the range of the data be checked for a shift before the estimation process.

defaultBiSectionSearchIntervalWidth

var defaultBiSectionSearchIntervalWidth: Double

Default size of the bi-section search interval around the initial Newton refined estimate. The default is 10.0.

defaultNumNewtonSteps

var defaultNumNewtonSteps: Int

The default number of Newton steps. The default is 10. On average 3.5 steps should provide 4 digit accuracy.

defaultZeroTolerance

var defaultZeroTolerance: Double

How close we consider a double is to 0.0 to consider it 0.0 Default is 0.001

desiredPrecision

var desiredPrecision: Double

Desired precision. The default is 0.0001.

open override val id: Int

label

open override var label: String?

maximumIterations

var maximumIterations: Int

Maximum allowed number of iterations. The default is 100

name

open override val name: String

names

open override val names: List<String>

The name to associate with each dimension of the array that is returned by estimate(). The names should be unique. The order of the list of names should match the order of elements in the returned array.

rvType

open override val rvType: RVParametersTypeIfc

The type of random variable for which this estimator estimates parameters.

Functions

estimate

open override fun estimate(data: DoubleArray): DoubleArray

If the estimation process is not successful, then an empty array is returned.

estimateParameters

open override fun estimateParameters(data: DoubleArray, statistics: StatisticIfc): EstimationResult

Estimates the parameters associated with some distribution. The returned EstimationResult needs to be consistent with the intent of the desired distribution. Note the meaning of the fields associated with EstimationResult